Showing papers in "Ingénierie Des Systèmes D'information in 2020"

Tools and Strategy for Distance Learning to Respond COVID-19 Pandemic in Indonesia

Abstract: Distance learning is a learning process that allows students and instructors are in different physical locations During the COVID-19 pandemic, various countries including Indonesia chose the distance learning option to avoid the spread of the pandemic However, the implementation of distance learning is not without problems In Indonesia, the problems to implement distance learning are enormous both in terms of equitable distribution of infrastructure and costs for accessing the internet To overcome these problems, research about best distance learning tools and its implementation strategy is really needed Therefore, this research aimed to analyze the strategies and tools for implementing distance learning that reliable and cost-effective for the student at the same time Based on research that has been conducted by analyzing questionnaire surveys of 256 students in Indonesia, this research recommends tools combination strategy consists of Google Classroom tool as a learning app, Whatsapp group as broadcast messaging and Zoom as a video conference media for distance learning This research also recommended to maximize the e-learning/learning app features for theoretical lessons and consider video conference just in practical lessons so that the distance learning costs become cheaper for students © 2020 International Information and Engineering Technology Association All rights reserved

Software Development Dengan Extreme Programming (XP) Pada Aplikasi Deteksi Kemiripan Judul Skripsi Berbasis Android

Abstract: To determine the topic or research title for the student's thesis, it is possible that the similarity of the title to the previous title, either accidentally or unintentionally. Therefore, the relevant section must check the titles submitted by students, so as to avoid plagiarism. The development of a thesis title similarity detection application is one of the efforts that can be made in solving this problem. The development of sosftware requires the right method to develop applications according to user needs. Extreme Programming (XP) is a part of the agile development approach based on iterative development, which is based on needs and solutions through collaboration among the development team. So, in this study, the development of a similarity detection application for the title of a thesis with the XP software development approach was carried out. Keywords - agile development, extreme programming, thesis

The Use of Edmodo Apps in Flipped Classroom Learning. How is the Students’ Creative Thinking Ability?

Abstract: 1 Primary Teacher Education, Universitas PGRI Madiun, Madiun 63118, Indonesia 2 Indonesian Language and Literature Education Department, IKIP PGRI Bojonegoro, Bojonegoro 62114, Indonesia 3 English Education Department, IKIP PGRI Bojonegoro, Bojonegoro 62114, Indonesia 4 Mathematics Education Department, IKIP PGRI Bojonegoro, Bojonegoro 62114, Indonesia 5 Graduate School of Universitas Sebelas Maret Surakarta, Surakarta 57126, Indonesia 6 Faculty of Teacher Training Education, Universitas Terbuka Jakarta, Jakarta 15418, Indonesia

An Early-Warning Model for Online Learners Based on User Portrait

Abstract: Received: 9 April 2020 Accepted: 16 July 2020 In the age of the Internet, online learning is an important learning strategy. At present, a large number of data on learning behavior have been generated on various online education platforms. It is difficult to grasp the learning situation of the numerous learners of these platforms according to the massive data. User portrait offers a possible solution to the problem. This paper firstly classifies the portrait of online learners into three dimensions, and constructs the tag system of learner portrait based on the data fields of online learning platform. Then, the learning behavior data of online learners were analyzed in details. Online learners were divided into multiple groups through data mining, and the learner portrait was generated. From the five dimensions of learner portrait, the learning situation was analyzed to master the learning information of learners. Based on the analysis results, the four-dimensional early-warning of learning situation was realized through sequence analysis and association rule mining. The research results provide a good reference for the improvement of online learning.

Analysis of the Impacts of Open Residential Communities on Road Traffic Based on AHP and Fuzzy Theory

Abstract: Received: 1 December 2019 Accepted: 18 February 2020 With the continuous urbanization in China, the number of private cars has grown rapidly in urban areas, leading to increasingly prominent road congestions. Urban road networks are wide but sparse, which can easily cause congestion. To tackle with this problem, the State Council proposed the idea of “open residential community” in 2016, which is to connect the roads within the residential community with external roads to densify the road networks and increase the area of branch roads, so as to mitigate urban road traffic pressure. In order to study the impacts of open residential communities on the traffic capacity of surrounding roads, this paper first establishes an evaluation indicator system for road traffic using a number of factors such as traffic density, traffic delay time, number of intersection conflicts, road congestion rate and road accessibility based on the analytic hierarchy process (AHP) theory, and also builds a fuzzy comprehensive evaluation model. Then, with the above evaluation indicator system and model, this paper employs the VISSIM traffic simulation technology to simulate the residential community for testing. Finally, it uses the grey relational algorithm to compare and analyze the test data, and obtains the following conclusions: for residential communities with a large area and large traffic volume and those with a small area and large traffic volume, the surrounding road traffic is significantly reduced, and the traffic pressure is greatly alleviated; the effect comes second for those with a large area and small traffic volume; and for those with a small area and small traffic volume, surrounding traffic sees no improvement.

Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method

Abstract: Received: 28 June 2020 Accepted: 2 August 2020 Alzheimer is a type of dementia disease that is common in older ages. This disease is a progressive form of neurological disease that causes the destruction of brain cells. Since Alzheimer's is a progressive disease, various problems increase over time. For this reason, it is very important to diagnose the disease early and start the treatment process. In this study, it was tried to determine at which stage the disease is or whether it is Alzheimer using brain images. CNN architectures are used to diagnose the disease. In addition, a hybrid method we have developed has been proposed. With the architectures used, it is classified in 4 stages according to the disease progression level. In the proposed hybrid model, the Resnet50 method is used as the basis. The results are obtained separately by Alexnet, Resnet50, Densenet201, Vgg16, and the Hybrid method we developed. An accuracy of 90% has been achieved with the developed hybrid model. Consequently, when other scientific paper in the literature are investigated, it is finalized that the hybrid model developed to diagnose Alzheimer’s disease has achieved the success achieved by other CNN architectures and even offers better results.

An Effective Recommendation System to Forecast the Best Educational Program Using Machine Learning Classification Algorithms

Abstract: Received: 10 July 2020 Accepted: 20 September 2020 After passing the 10 class, every student is eager to know which educational program will be the best for their higher education to match their career goal. Sometimes, they are very much confused to decide the best path for their higher education, and they need help to determine the best suitable academic program to develop their careers and achieve their goal. So, we introduce an effective recommendation system to forecast each student's best educational program for their career development. This proposed research is accomplished by utilizing machine learning (ML) approaches to forecast every student's best academic path based on their past academic performances and recommend them the best suitable academic program for their higher studies. Class 10 standard passing student data are supplied to this automated system, and a correlation-based feature selection approach is applied to extract the relevant features for each academic program. This study utilizes multiple ML algorithms to provide the best results and forecast each student's academic performance and select the best model based on their performance for each educational program. Hence, the best-selected model and related features are involved in the recommendation process to provide the best suitable academic path for achieving every student's career goals.

Android Botnet Detection Using Machine Learning

Abstract: Received: 7 November 2019 Accepted: 10 January 2020 A botnet is a network of agreed nodes spreading malware software, usually installed by all varieties of attacking methods likes worms, Trojan horses, and viruses. Many techniques have recently been proposed to block mobile malware or detect it. But our model is different on another that proposed before, it focused on 81 attributes that collected from network traffic features. We tested ten of android botnet, which are Beanbot, Biige, Fakeinst, FakeMart, FakeNotify, Jifake, Mazarbot, Nandrobox, Plankton, and SMSsniffer using Weka machine learning. We have 32762 instances, which classified as attack and not attack. We used WEKA machine learning and we tested SMO, Random Tree, J48, Naïve Bayes and LMT algorithms. The best result to classify the botnet attack was 85%. The contribution of this paper is detected major of android botnet in different scenario because we are using 81 attributes. In future work, we will attach new sub algorithm in machine learning, to improve accuracy of the result of detecting more mobile malware.

Classification of Malaria Cell Images with Deep Learning Architectures

Abstract: Received: 10 November 2019 Accepted: 3 January 2020 Malaria is a contagious disease caused by the infection of erythrocytes by Plasmodium parasites, which are transmitted to human by parasitic female anopheles’ mosquitoes during feeding. Malaria is a type of infection that can be fatal if left untreated. It is very important to classify malaria virus images quickly and accurately using computer-aided systems. Because there are not enough personnel in each health unit to perform this procedure, traditional methods are both time consuming and open to errors. Once malaria images have been classified, it will be easier to diagnose malaria virus related diseases. Multiple methods have been developed to process large amounts of data. In particular, deep learning methods are frequently used for classification. In this paper, Convolutional Neural Networks (CNN) have been used to classify malaria images as healthy and parasited. Then, medium filter and gauss filter are applied to the original dataset. When classifying malaria data, the highest accuracy rate is achieved in the DenseNet201 architecture with gaussian filtered data of 97.83%. It is observed that the result obtained with the preprocessed data are higher. The application is implemented in the Matlab environment and works independently of the size of the images in the data set.

Regression Analysis on the Influencing Factors of the Acceptance of Online Education Platform among College Students

Abstract: Received: 8 June 2020 Accepted: 12 September 2020 With the proliferation of the fifth generation (5G) communication technology, another boom of online education will come, and reshape our traditional learning model. Inspired by the literature on online education platform, this paper establishes a model for the factors affecting the acceptance of online education platform among college students based on the theory of planned behavior (TPB), and put forward several hypotheses on the influence of multiple factors over the acceptance. Then, a scientific questionnaire was designed and distributed online to college students. The survey data were subject to descriptive analysis and correlation analysis. The results show that college students have considered online education platforms an important learning tool; the acceptance of online education platform among college students is positively affected by such factors as personal value, course satisfaction, teacher quality, social influence, and self-efficacy. The research results provide a good reference for the development of online education in China.

Classification of Brain Tumors Using Convolutional Neural Network over Various SVM Methods

Abstract: Received: 21 May 2020 Accepted: 23 July 2020 A computer-based method is presented in this paper to define brain tumor using MRI images. The main classification motive is to identify a brain into a healthy brain or classify a brain with a tumor when a patient’s MRI images are given. Magnetic Resonance Imaging (MRI) is an important one among the common imaging treatments, which presents more detailed brain tumor identification information and provides detailed pictures of inside your body other than computed tomography (CT). Currently, CNNs is a famous technique to deal with most of the problems with image classification as they provide greater accuracy compared to other classifiers. Hbridized CNN has been used in this work. It consists of three convolution layers and three max pooling layers which could provide outrated performance. Images from open databases such as BRATS were tested on brain MRI images. The proposed model has given the improved performance over the existing model with an accuracy of 96.15%.

Analysis on Course Scores of Learners of Online Teaching Platforms Based on Data Mining

Abstract: Received: 1 June 2020 Accepted: 24 August 2020 After years of development, online teaching platforms (OLPs) have accumulated a huge amount of data on student scores. To effectively mine out the useful knowledge and information behind the massive data, this paper puts forward a course score analysis model for OLP learners based on data mining. Firstly, the score features of OLP learners were classified, and the calculation method of computational features was presented. Then, the score features were clustered through expectation maximization (EM) clustering, which has the advantage of unsupervised learning. Moreover, the salient features were obtained through principal component analysis (PCA). Finally, the support vector machine (SVM) prediction algorithm, a supervised learning method, was constructed, and merged with the clustering algorithm to realize accurate classification of the course scores of OLP learners. The effectiveness of the proposed method was proved through experiments. Based on the correlation between learner scores and courses, this research enables teachers to improve current teaching models and methods.

A Semi-supervised Deep Auto-encoder Based Intrusion Detection for IoT

Abstract: 1 Department of Computer Science, Faculty of Exact Sciences, University of Bejaia, Bejaia 06000, Algeria 2 Mechatronics Laboratory E1764200, Optics and Precision Mechanics Institute, University of Sétif 1, Sétif 19000, Algeria 3 College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, UAE 4 Mathematics and Computer Science Department, University of Algiers 1, Algiers 16000, Algeria

An automated CAD system of CT chest images for COVID-19 based on genetic algorithm and K-nearest neighbor classifier

Abstract: The detection of COVID-19 from computed tomography (CT) scans suffered from inaccuracies due to its difficulty in data acquisition and radiologist errors Therefore, a fully automated computer-aided detection (CAD) system is proposed to detect coronavirus versus non-coronavirus images In this paper, a total of 200 images for coronavirus and non-coronavirus are employed based on 90% for training images and 10% testing images The proposed system comprised five stages for organizing the virus prevalence In the first stage, the images are preprocessed by thresholding-based lung segmentation Afterward, the feature extraction technique was performed on segmented images, while the genetic algorithm performed on sixty-four extracted features to adopt the superior features In the final stage, the K-nearest neighbor (KNN) and decision tree are applied for COVID-19 classification The findings of this paper confirmed that the KNN classifier with K=3 is accomplished for COVID-19 detection with high accuracy of 100% on CT images However, the decision tree for COVID-19 classification is achieved 95% accuracy This system is used to facilitate the radiologist’s role in the prediction of COVID-19 images This system will prove to be valuable to the research community working on automation of COVID-19 images prediction © 2020 International Information and Engineering Technology Association All rights reserved

An Operation and Maintenance Strategy of Intelligent Building Information Model Data Based on Cloud Computing

Abstract: Received: 29 March 2020 Accepted: 21 June 2020 The operation and maintenance (O&M) of intelligent building information model (IBIM), as an important aspect of modern building informatization, plays a critical role in the construction of smart cities and the renovation of modern buildings. Due to the complex structure and sheer size of IBIM data, the O&M system of IBIM faces a huge workload and a high cost. Based on cloud computing, this paper proposes an O&M strategy for IBIM data, which meets the needs for stable management of massive data, enables three-dimensional (3D) collaborative visualization of building information model (BIM), improves the efficiency of data storage, scheduling, and O&M. Firstly, the basic structure of IBIM data was standardized according to The Industry Foundation Classes (IFC) (ISO 16739-1:2018). Then, the features were sampled from the standardized IBIM cloud data. After that, the storage data were subject to routing, coding, matching feature compression, and adaptive attribute clustering. On this basis, an optimization model was established for the storage structure of IBIM cloud data. Finally, a batch feature extraction method was designed for 3D structure distribution of job-based 3D cloud storage model. The proposed strategy was proved effective through experiments. The research results provide a reference for applying 3D cloud storage model in other fields.

Penerapan Metode Profile Matching Dalam Sistem Penunjang Keputusan Pemilihan Karyawan Terbaik Pada Lembaga Penyiaran Publik Televisi Republik Indonesia

Abstract: Penilaian untuk pemilihan karyawan terbaik biasanya masih dilakukan secara subyektif dan manual, dan kurangnya sistem proses pengambilan keputusan formal ini dapat menjadi masalah. Studi ini bertujuan untuk membangun sistem pendukung keputusan untuk pemilihan karyawan terbaik sesuai dengan persyaratan Lembaga Penyiaran Publik Televisi Republik Indonesia. Metode yang digunakan dalam penelitian ini adalah Profile Matching dengan kriteria yang telah ditentukan oleh Institusi tersebut, yaitu: Kehadiran, Perilaku, Tanggung Jawab, Kerjasama dan Produktivitas. Sistem ini, dikembangkan dengan menggunakan Rational Unified Process (RUP) dan juga alat pengembangan sistem yaitu Unified Modeling Language 2.0 (UML). Hasil dari pengujian didapatkan akurasi sistem mencapai 100%, yaitu semua keluaran sistem untuk rangking karyawan terbaik sama dengan yang dipilih oleh kepala Divisi SDM. Sehingga bisa disimpulkan bahwa hasil penelitian ini dapat dijadikan sebagai alat bantu untuk memilih karyawan terbaik di Lembaga Penyiaran public Televisi Republik Indonesia.

High Throughput and Thermal Aware Routing Protocol (HTTRP) for Wireless Body Area Networks

Abstract: Received: 9 October 2019 Accepted: 26 December 2019 One of the major applications of sensor networks in the near future will be in the area of biomedical research. Wireless Body Area Networks (WBANs) are composed of implanted biosensors for health monitoring and diagnostic purposes. The communication between these sensors is made in a wireless way at the base of the radio waves. The sensors' activity produces heat causing a temperature rise. The high-temperature rise of the sensors for a prolonged period might damage the surrounding tissues. Various routing protocols have been proposed in the literature to remedy this problem. These protocols tried to perform routing based on the SHR algorithm while avoiding hot-spots nodes. However, the energy of sensor nodes located in this shorter path is quickly exhausted and by the way, the whole network lifetime is influenced. In this work, we propose HTTRP, a new routing protocol for WBANs introducing a new route selection mechanism that aims to reduce the overheating of sensors and balance their energy consumption. This mechanism is based on a function that considers the residual energy of sensor nodes and their temperature when choosing the next relay node. The carried out simulation results show that our HTTRP protocol has better performance in terms of network lifetime, charge balancing, temperature rise, and throughput compared to a representative of TARP that is TARA protocol.

Secure Data Uploading and Accessing Sensitive Data Using Time Level Locked Encryption to Provide an Efficient Cloud Framework

Abstract: Received: 9 March 2020 Accepted: 26 May 2020 Executing cloud computing participate from multiple points of view for Web-based overseeing commitments to mark various issues. Here the assurance and data security has considered into a significant issue with downside that limits numerous applications that identified with cloud. These days cloud computing has been generally perceived as one of the most compelling data advances in view of its phenomenal focal points. Regardless of its generally perceived social and monetary advantages, in cloud computing clients lose the immediate control of their information and totally depend on the cloud to deal with their information and calculation, which raises huge security and protection concerns and is one of the significant obstructions to the appropriation of open cloud by numerous associations and people. In the proposed work a Time Level Locked Encryption (TLLE) framework is designed for deduplication of huge documents to accomplish space-proficient capacity in cloud and also to monitor data uploading and data access. The proposed model is compared with the traditional AES encryption model and the results show that the proposed model is exhibiting better performance.

AI Data Driven Approach-Based Endogenous Inputs for Global Solar Radiation Forecasting

Abstract: Received: 16 November 2019 Accepted: 3 January 2020 Controlling the random nature of renewable energy sources such as solar radiation at ground, allows electric grid operators to better integrate it. In this paper, an intelligent datadriven model based on artificial neural network with autoregressive input sequence is developed to forecast the global solar radiation (GSR) time series on a half hour resolution in the site of Agdal, Marrakesh, Morocco. The database that is used to create this model was divided into two subsets. The first subset is used for training the proposed model on the data measured during the year 2008 by adopting three efficient optimizers (levenbergmarquardt, resilient backpropagation, and scaled conjugate gradient). The second subset is used for testing the efficiency and the robustness of the developed model to generate accurate predictions during the next six years (from 2009 to 2014). The obtained results demonstrate the accuracy and the stability of the proposed data-driven model to perform prediction in case of GSR measurements intermittence or sensor damage.

Penerapan algoritma apriori pada data penjualan barbar warehouse

Abstract: Dunia bisnis para pelaku usaha saling bersaing dalam berinovasi dan melakukan strategi untuk menarik pelanggan guna meningkatkan penjualan. Barbar Warehouse merupakan sebuah retail online yang menjual produk fashion, kebutuhan bayi seperti popok, seprei untuk kebutuhan para pelanggan. Barbar Warehouse sendiri mempunyai 5 toko yang terdaftar dalam marketplace Zilingo.com. Strategi Barbar Warehouse sebagai retail online melakukan pembelanjaan dalam 3 bulan sekali, dalam waktu tersebut Barbar Warehouse menerima total pesanan sebesar 42.555 dari 5 toko yang terdaftar. Data transaksi penjualan setiap harinya selalu bertambah, semakin banyak data transaksi tersimpan menyebabkan penyimpanan data menjadi besar. Data transaksi penjualan dimanfaatkan dan diolah menjadi informasi dalam meningkatkan penjualan produk. Barbar Warehouse memerlukan sebuah metode untuk menganalisa pangsa pasar melalui pola penjualan untuk mengetahui kecenderungan konsumen dalam membeli barang. Metode yang digunakan dalam penelitian ini adalah data mining algoritma apriori pada data penjualan Barbar Warehouse dalam 3 bulan dan diolah menggunakan Rapid Minner. Hasil pengolahan data dengan Rapid Minner menunjukkan bahwa penjualan produk yang paling banyak terjual yaitu pada kategori beding (seprei) dan watches ( kacamata).

The Diagnosis of Online Game Addiction on Indonesian Adolescent Using Certainty Factor Method

Abstract: Received: 2 December 2019 Accepted: 19 February 2020 This research discusses about online games which become ones of the most popular commodities, particularly among adolescents. It is suggested that they prefer spending time by playing online games to studying. Consequently, the phenomenon of online game addiction gets significant. The study aimed to explain and describe the use of Certainty Factor Method with expert system to diagnose online game addiction on adolescents. Its setting was Java island by involving some students as subject of the study. Survey method was employed to obtain the data while its instrument in terms of questionnaire was provided in Google forms and distributed to the students. An interactive qualitative approach was deployed to analyze the data combined with certainty factor method to show the level of game online addiction. The result shows from the sample of data which have analyzed addiction level toward online games is relatively moderate. Most of the users are adolescents between 12 and 15 years old and spend about 4 until 6 hours in a day. This condition becomes worse if there is no treatment for adolescents. Hence, there is a need of parents’ control and awareness toward children’s activity in playing online games since it renders negative impacts particularly on children’s learning activity and motor development.

Fire Safety Assessment of High-Rise Buildings Based on Fuzzy Theory and Radial Basis Function Neural Network

Abstract: Received: 10 November 2019 Accepted: 5 February 2020 Featuring complex functions, dense population, and large span, high-rise buildings are an iconic product of economic and technical growths in modern society. Fire poses an imminent threat to high-rise buildings. Once a high-rise building catches fire, the loss of life and property will be incalculable. However, the traditional assessment methods for fire safety of high-rise buildings are incapable of handling the complex influencing factors. To solve the problem, this paper combines the fuzzy logic inference system and radial basis function neural network (RBFNN) into an intelligent assessment method mimicking the nonlinear inference process of fire safety experts. Firstly, the factors affecting the fire safety of high-rise buildings were quantified, and the relevant rating standard was established. Next, the classic statistics were transformed into fuzzy indices, using the fuzzy logic system. To avoid the local minimum trap, the RBFNN was adopted to replace the traditional backpropagation neural network (BPNN), and integrated with the fuzzy logic system, creating an adaptive fuzzy-RBFNN to assess the fire safety of high-rise buildings. The proposed network was trained by numerous expert evaluation samples, and verified by examples. The simulation results show that the proposed network could mimic the nonlinear inference process of fire safety experts, and evaluate the fire safety of high-rise buildings in real time with little error. The research results provide new insights on the application of artificial intelligence (AI) in fire safety assessment.

Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

Abstract: Received: 5 June 2020 Accepted: 29 September 2020 The traditional knowledge service systems have nonuniform data structures. Some data are structured, while some are semi-structured and even non-structured. Big data technology helps to optimize the integration and retrieval of the massive data on library and information (L&I), making it possible to classify the resources and optimize the configuration of L&I resource platforms according to user demand. Therefore, this paper introduces the new information service model of big data resources and knowledge services to the processing of L&I data. Firstly, the data storage structure and relationship model of the L&I resource platform were established, and used to sample and integrate the keywords of resource retrieval. Next, an L&I resource classification model was constructed based on support vector machine (SVM), and applied to extract and quantify the attributes of the keywords of resource retrieval. After that, a knowledge aggregation model was developed for a complex network of multiple L&I resource platforms. Experimental results demonstrate the effectiveness of the proposed knowledge aggregation model. The research findings provide a reference for the application of data mining in resource classification.

Evaluasi Sistem Informasi Cibugary Dalam Peningkatan Promosi Eduwisata Dengan Architecture Capability Maturity Model Score

Abstract: Pemasaran industri wisata pada masa ini memanfaatkan dukungan teknologi berupa sistem informasi, dengan tujuan untuk mempermudah kegiatan, salah satunya adalah industri pariwisata pada peternakan wilayah Pondok Rangon. Penelitian ini merupakan usulan dalam tatakelola teknologi informasi untuk industri pariwisata pada peternakan wilayah Pondok Rangon sebagai arahan bagi pengembangan pemasaran industri pariwisata dan menghilangkan selisih dari kedua keadaan yang ada sekaligus mencapai keadaan pada arsitektur target. Tujuan dari penelitian ini untuk mengukur Sistem Informasiyang diterapkan sebagai pemasaran industri pariwisata menggunakan Architecture Capability Maturity Model Score ( ACMM ) framework dan menghasilkan kerangka dasar analisis berupa narasi serta nilai sebagai acuan evaluasi Sistem Informasiyang diterapkan apakah sudah berhasil meningkatkan promosi eduwisata. EACMM framework terdiri dari enam tingkat kematangan dan sembilan elemen arsitektur. Keenam tingkatan tersebut adalah: 0–Existent, 1–Initial, 2–Repeatable, 3–Define, 4–Manage, 5–Optimised. Kesembilan elemen arsitektur enterprise adalah: Proses Arsitektur, Pengembangan Arsitektur, Linkage Bisnis, Keterlibatan manajemen senior, Operasi partisipasi Unit, Arsitektur komunikasi, Keamanan TI, Arsitektur pemerintahan, Investasi TI dan strategi akuisisi. Dua metode pelengkap yang digunakan dalam ACMM untuk menghitung rating jatuh tempo. Metode pertama memperoleh sebuah perusahaan rata-rata tertimbang tingkat kematangan arsitektur. Metode kedua menunjukkan persentase yang dicapai pada setiap tingkat kematangan untuk Sembilan elemen arsitektur.

Deep learning-based forecast and warning of floods in Klang River, Malaysia

Abstract: Long short-term memory (LSTM) networks are state of the art technique for time-series sequence learning. They are less commonly applied to the hydrological engineering area especially for river water level time-series data for flood warning and forecasting systems. This paper examines an LSTM network for forecasting the river water level in Klang river basin, Malaysia. The river water level contains of two features dimension and one time-series observed data, in this study, prediction responses for river water level data using a trained recurrent neural network and update the network state function is applied. The radial basis function neural network (RBFNN) in order to get comparison of the generalization solving problem also performed. The performance indicates with the root mean square error, RMSE 0.0253 and coefficient of determination value, R2 0.9815 are closely accurate when updating the network state compared with the RBFNN results. These results verified that the LSTM network with specified training set options is a promising alternative technique to the solution of flood modelling and forecasting problems.

User-Review Oriented Social Recommender System for Event Planning

Abstract: Received: 14 June 2020 Accepted: 25 September 2020 Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

Integrasi E-Government Kabupaten Sidoarjo dengan Service Oriented Architecture (SOA)

Abstract: Duplikasi data, data tidak sinkron, dan pembengkakan dana merupakan masalah utama dari sistem pelayanan kepemerintahan ( e-government ) saat ini. Hal ini disebabkan karena belum adanya integrasi antar sistem e-government pada pemerintah daerah (pemda). Penerapan integrasi data dapat menghemat dana pembuatan dan operasional hingga 7 triliun pada setiap pemerintah daerah dan dapat mempercepat proses interaksi dan komunikasi antar dinas pada pemerintah daerah. Pada penelitian ini bertujuan untuk mengembangkan sebuah e-government yang terintegrasi dari berbagai layanan yang ada pada pelayanan kepemerintahan Kabupaten Sidoarjo. Metode yang digunakan dalam mengembangkan integrasi ini yaitu menggunakan Metode Service Oriented Architecture (SOA). Metode ini dipilih karena mempunyai banyak kelebihan, seperti : reusable , mudah dikelola, skalabilitas yang tinggi, dan mudah dikembangkan. Pada proses pengembangan e-government mempunyai 3 tahap utama, yaitu : pembuatan database, pembuatan service, dan penerapan service pada frontend . Hasil dari penelitian ini yaitu berupa kesimpulan terhadap kemampuan metode yang digunakan dalam integrasi e-government dan sebuah aplikasi pelayanan e-government

Brain Tumor Detection Using Deep Features in the Latent Space

Abstract: Received: 3 December 2019 Accepted: 28 February 2020 Tumor grown in the human brains is one of the significant reasons that lead to loss of lives globally. Tumor is malignant collection of cells that grow in the human body. If these tumors grow in the brain, then they are called as brain tumors. Every year large number of human lives are lost due to this disease. Early detection of the disease might save the lives but requires experienced clinicians and diagnostic procedure that requires time and is very expensive. Therefore, there is a requirement for a robust system that automates the process of tumor identification. The idea behind this paper is to diagnose brain tumors by identifying the affected regions from the brain MRI images using machine learning approaches. In the proposed approach, prominent features of the tumor images are collected by passing them through a pre-trained Convolutional Network, VGG16. We observe that SVM gives better accuracy than other models. Though we achieve 84% accuracy, we feel the performance is not satisfactory. To make the model more robust, we obtain the most discriminant features, by applying Linear Discriminant Analysis (LDA) on the features obtained from VGG16. We use different conventional models like logistic regression, K-Nearest neighbor classifier (KNN), Perceptron learning, Multi Layered Perceptron (MLP) and Support Vector Machine (SVM) for the comparison study of the tumor image classification task. The proposed model leads to an accuracy of 100% as deep features extract important characteristics of the data and further LDA projects the data onto the most discriminant directions.